{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA on manual_title\n",
    "\n",
    "Now that we've created a manually-checked subset of the title metadata, let's do some exploratory analysis. Among other things, this can help us understand the level of error in the larger dataset.\n",
    "\n",
    "We start by importing needed modules, and loading the data itself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from collections import Counter\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "all = pd.read_csv('manual_title_subset.tsv', sep = '\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bootstrap resampling function\n",
    "\n",
    "We're going to want to have some quick and dirty error bars on the ratios plotted below. We can get that through bootstrap resampling.\n",
    "\n",
    "All the error bars below will represent 90% confidence intervals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bootstrap_ratio(numtrue, numfalse):\n",
    "    population = [True] * numtrue + [False] * numfalse\n",
    "    results = []\n",
    "    \n",
    "    for i in range(1000):\n",
    "        sample = np.random.choice(population, size = len(population), replace = True)\n",
    "        ratio = sum(sample) / len(sample)\n",
    "        results.append(ratio)\n",
    "    \n",
    "    results.sort()\n",
    "    return results[49], results[950]\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Percentage of volumes that are actually fiction\n",
    "\n",
    "One obvious question: how precise is our sample of fiction, and--even more crucially--how does the level of error vary across time?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AFoQOAABgC0IHAACwBaEDAADYgtABAABsQegAAAC2IHQAAABbEDoAAIAtCB0A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\nmiIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2d81c198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "timeaxis = []\n",
    "percentages = []\n",
    "maxima = []\n",
    "minima = []\n",
    "\n",
    "for decade in range(1800, 2010, 15):\n",
    "    in_decade = all.loc[(all.firstpub >= decade) & \n",
    "                        (all.firstpub < (decade + 15)), : ]\n",
    "    ficvols = sum((in_decade.category == 'longfiction') | \n",
    "                  (in_decade.category == 'shortfiction') | \n",
    "                  (in_decade.category == 'juvenile'))\n",
    "    nonficvols = sum((in_decade.category == 'notfiction')  | \n",
    "                     (in_decade.category == 'poetry') |\n",
    "                    (in_decade.category == 'drama'))\n",
    "    pct = ficvols / (ficvols + nonficvols)\n",
    "    timeaxis.append(np.mean(in_decade.firstpub))\n",
    "    percentages.append(pct)\n",
    "    \n",
    "    minimum, maximum = bootstrap_ratio(ficvols, nonficvols)\n",
    "    maxima.append(maximum)\n",
    "    minima.append(minimum)\n",
    "\n",
    "plt.figure(figsize = (9, 6))\n",
    "sns.set(font_scale= 1.3)\n",
    "sns.set_style(\"whitegrid\")\n",
    "plt.xlim(1800,2010)\n",
    "plt.ylim(0.5, 1)\n",
    "downward = np.array(percentages) - np.array(minima)\n",
    "upward = np.array(maxima) - np.array(percentages)\n",
    "plt.errorbar(timeaxis, percentages, yerr = [downward, upward], fmt='--o', \n",
    "             ecolor = 'k', color = 'b', label = 'fraction of titles that are fiction')\n",
    "plt.legend(loc='lower right')\n",
    "plt.savefig('figures/titlesubset/pctfic.png', bbox_inches = 'tight', dpi = 300)\n",
    "plt.show() \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you squint at that graph, you may suspect that accuracy increases slightly across time. There may be something to that hypothesis, but it's not statistically significant with this number of bins. *r* = .34, *p* = 0.24."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.33629139929475022, 0.23975110537249483)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import pearsonr\n",
    "pearsonr(timeaxis, percentages)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could increase the number of bins, but that would also increase the size of the error bars. So, in short, it's not a particularly dramatic trend--which is good, because dramatic changes in the accuracy of our classifier could produce confounding effects when people use the titlemeta.tsv dataset to pose other questions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Percentage that are novels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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L9OWXX7pu79u3T/369dMnn3yioKCgShcOvi8tLY36raGo25qN+q25qrpuz9aY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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2d843e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "timeaxis = []\n",
    "percentages = []\n",
    "maxima = []\n",
    "minima = []\n",
    "\n",
    "for decade in range(1800, 2010, 15):\n",
    "    in_decade = all.loc[(all.firstpub >= decade) & \n",
    "                        (all.firstpub < (decade + 15)), : ]\n",
    "    categoryvols = sum((in_decade.category == 'longfiction'))\n",
    "    notcatvols = sum((in_decade.category == 'notfiction')  | \n",
    "                     (in_decade.category == 'poetry') |\n",
    "                    (in_decade.category == 'drama') |\n",
    "                    (in_decade.category == 'shortfiction') | \n",
    "                  (in_decade.category == 'juvenile'))\n",
    "    pct = categoryvols / (categoryvols + notcatvols)\n",
    "    timeaxis.append(np.mean(in_decade.firstpub))\n",
    "    percentages.append(pct)\n",
    "    \n",
    "    minimum, maximum = bootstrap_ratio(categoryvols, notcatvols)\n",
    "    maxima.append(maximum)\n",
    "    minima.append(minimum)\n",
    "\n",
    "plt.figure(figsize = (9, 6))\n",
    "plt.xlim(1800,2010)\n",
    "plt.ylim(0, 1)\n",
    "downward = np.array(percentages) - np.array(minima)\n",
    "upward = np.array(maxima) - np.array(percentages)\n",
    "plt.errorbar(timeaxis, percentages, yerr = [downward, upward], fmt='--o', \n",
    "             ecolor = 'k', color = 'b', label = 'fraction of titles that are novels')\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.savefig('figures/titlesubset/pctnovels.png', bbox_inches = 'tight', dpi = 300)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Percentage that are juvenile fiction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JaNNGzvJKlsXBAQgOVl0FmUtkpPw+aVIqCgraoEkTDWJi7i4nYiAh5X75RX7X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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2d690e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "timeaxis = []\n",
    "percentages = []\n",
    "maxima = []\n",
    "minima = []\n",
    "\n",
    "for decade in range(1800, 2010, 15):\n",
    "    in_decade = all.loc[(all.firstpub >= decade) & \n",
    "                        (all.firstpub < (decade + 15)), : ]\n",
    "    categoryvols = sum((in_decade.category == 'juvenile'))\n",
    "    notcatvols = sum((in_decade.category == 'notfiction')  | \n",
    "                     (in_decade.category == 'poetry') |\n",
    "                    (in_decade.category == 'drama') |\n",
    "                    (in_decade.category == 'shortfiction') | \n",
    "                  (in_decade.category == 'longfiction'))\n",
    "    pct = categoryvols / (categoryvols + notcatvols)\n",
    "    timeaxis.append(np.mean(in_decade.firstpub))\n",
    "    percentages.append(pct)\n",
    "    \n",
    "    minimum, maximum = bootstrap_ratio(categoryvols, notcatvols)\n",
    "    maxima.append(maximum)\n",
    "    minima.append(minimum)\n",
    "\n",
    "plt.figure(figsize = (9, 6))\n",
    "plt.xlim(1800,2010)\n",
    "plt.ylim(-0.01, 0.2)\n",
    "downward = np.array(percentages) - np.array(minima)\n",
    "upward = np.array(maxima) - np.array(percentages)\n",
    "plt.errorbar(timeaxis, percentages, yerr = [downward, upward], fmt='--o', \n",
    "             ecolor = 'k', color = 'b', label = 'fraction of titles that are juvenile fiction')\n",
    "plt.legend(loc = 'upper left')\n",
    "plt.savefig('figures/titlesubset/pctjuvenile.png', bbox_inches = 'tight', dpi = 300)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Relative to the size of the category, these variations are very large. They could be worth keeping in mind, if you're pursuing a question where works for a juvenile audience are likely to be starkly different from other fiction, especially if you're counting exceptional works rather than calculating means. For this reason, I will provide some probabilistic guidance to help researchers filter out unmarked juvenile works in the larger fiction dataset.\n",
    "\n",
    "Relative to the size of the overall dataset, the variation is not huge; we'll see later how much difference it makes to some typical diachronic questions. But it is notable that accuracy improves in the last couple of decades, probably because genre was actually **recorded** in the last thirty years, improving my ability to filter out juvenile works."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accuracy of estimated dates\n",
    "\n",
    "Another source of error in our larger dataset is that volumes are not dated to the date of first publication. The best I can do is something I call ```latestcomp```--latest possible date of composition, which is basically an intersection of earliest attested publication date for the title, and author's death date.\n",
    "\n",
    "How much error does this cause? Let's look at the percentage of books where ```latestcomp``` is more than ten years after true ```firstpub```.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Bbm5HAVSiRw9xOz7eOuer7w9ydHQ0OnbsiAEDBmDgwIEoKCjAkCFDUFhYCAB48803cfbs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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2d8925f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "timeaxis = []\n",
    "percentages = []\n",
    "maxima = []\n",
    "minima = []\n",
    "\n",
    "for decade in range(1800, 2010, 15):\n",
    "    in_decade = all.loc[(all.latestcomp >= decade) & \n",
    "                        (all.latestcomp < (decade + 15)), : ]\n",
    "    \n",
    "    timeaxis.append(np.mean(in_decade.latestcomp))\n",
    "    diffs = np.absolute(in_decade.latestcomp - in_decade.firstpub)\n",
    "    misdated = sum(diffs > 10)\n",
    "    notmisdated = len(diffs) - misdated\n",
    "    percentages.append(misdated / (misdated + notmisdated))\n",
    "    minimum, maximum = bootstrap_ratio(misdated, notmisdated)\n",
    "    maxima.append(maximum)\n",
    "    minima.append(minimum)\n",
    "\n",
    "plt.figure(figsize = (9, 6))\n",
    "plt.xlim(1800,2020)\n",
    "plt.ylim(0,0.18)\n",
    "downward = np.array(percentages) - np.array(minima)\n",
    "upward = np.array(maxima) - np.array(percentages)\n",
    "plt.errorbar(timeaxis, percentages, yerr = [downward, upward], fmt='--o', \n",
    "             ecolor = 'k', color = 'b', label = 'fraction of titles with a lag > 10yrs')\n",
    "plt.legend(loc = 'upper left')\n",
    "plt.savefig('figures/titlesubset/offby10.png', bbox_inches = 'tight', dpi = 300)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basically, there is at all times a moderate-sized (4-10%) chunk of the collection where ```latestcomp``` significantly misses date of first publication. Once again, accuracy may improve in the last thirty years or so, but there is no statistically significant trend.\n",
    "\n",
    "Of course, this is just reporting the fraction of volumes where there is any error greater than 10 years. The actual size of the error can vary hugely, covering everything from off-by-11-year novels to medieval sagas, or Plutarch! We could calculate the \"mean error\" or \"median error,\" but those tell us very little. The median error is always zero. The mean error varies greatly but the variation is driven entirely by outliers. This may be more useful:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Y5RAREZEBzDaYAMCgO+NMTvJyDhERkUkw62AyuHf9OBNOtEZERGQKzDqYdHCQ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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b894748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diffs = all.latestcomp - all.firstpub\n",
    "sizes = []\n",
    "ratios = []\n",
    "\n",
    "for errorsize in range(100):\n",
    "    errorcount = sum(diffs > errorsize)\n",
    "    sizes.append(errorsize)\n",
    "    ratios.append(errorcount / len(diffs))\n",
    "\n",
    "plt.figure(figsize = (9, 6))\n",
    "plt.plot(sizes, ratios, label = 'fraction of titles with errors of of this size or greater')\n",
    "plt.xlabel('size of difference, in years')\n",
    "plt.xlim(-2, 102)\n",
    "plt.legend(loc = 'upper right')\n",
    "plt.savefig('figures/titlesubset/errorcurve.png', bbox_inches = 'tight', dpi = 300)\n",
    "plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def isfiction(astring):\n",
    "    ''' Note that this doesn't count juvenile fiction.\n",
    "    '''\n",
    "    \n",
    "    if pd.isnull(astring):\n",
    "        return 'not'\n",
    "\n",
    "    if 'longfiction' in astring or 'shortfiction' in astring:\n",
    "            return 'fic'\n",
    "    else:\n",
    "        return 'not'\n",
    "\n",
    "all = all.assign(isfic = all.category.map(isfiction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+HgMHDrT4uOvXr6OgoAARERHo06fP47W6HERGauHjk4xevd4D8AaAwVCpBPzr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\nOHsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118295f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "timeaxis = []\n",
    "percentages = []\n",
    "minima = []\n",
    "maxima = []\n",
    "\n",
    "for decade in range(1800, 2010, 15):\n",
    "    in_decade = all.loc[(all.firstpub >= decade) & (all.firstpub < (decade + 15)) & ((all.isfic == 'fic')), : ]\n",
    "    masculine = sum(in_decade.gender == 'm') \n",
    "    feminine = sum(in_decade.gender == 'f')\n",
    "    pct = feminine / (masculine + feminine)\n",
    "    timeaxis.append(np.mean(in_decade.firstpub))\n",
    "    percentages.append(pct)\n",
    "    minimum, maximum = bootstrap_ratio(feminine, masculine)\n",
    "    maxima.append(maximum)\n",
    "    minima.append(minimum)\n",
    "\n",
    "plt.figure(figsize = (9, 6))\n",
    "plt.xlim(1800,2010)\n",
    "plt.ylim(0.0, 0.6)\n",
    "plt.title('Percentage of fiction by women')\n",
    "downward = np.array(percentages) - np.array(minima)\n",
    "upward = np.array(maxima) - np.array(percentages)\n",
    "plt.errorbar(timeaxis, percentages, yerr = [downward, upward], fmt='--o', ecolor = 'k', color = 'b')\n",
    "# plt.savefig('figures/titlesubset/pctwomen.png')\n",
    "plt.show() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weighted = pd.read_csv('weighted_subset.tsv', sep = '\\t')\n",
    "weighted = weighted.assign(isfic = weighted.category.map(isfiction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "weighttimes = []\n",
    "weightpcts = []\n",
    "\n",
    "for decade in range(1800, 2010, 15):\n",
    "    in_decade = weighted.loc[(weighted.firstpub >= decade) & (weighted.firstpub < (decade + 15)) & ((weighted.isfic == 'fic')), : ]\n",
    "    masculine = sum(in_decade.gender == 'm') \n",
    "    feminine = sum(in_decade.gender == 'f')\n",
    "    pct = feminine / (masculine + feminine)\n",
    "    weighttimes.append(np.mean(in_decade.firstpub))\n",
    "    weightpcts.append(pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "usuk = []\n",
    "\n",
    "for decade in range(1800, 2010, 15):\n",
    "    in_decade = all.loc[(all.firstpub >= decade) & (all.firstpub < (decade + 15)) &\n",
    "                        (all.isfic == 'fic') &\n",
    "                        ((all.nationality == 'uk') | (all.nationality == 'us')), : ]\n",
    "    masculine = sum(in_decade.gender == 'm') \n",
    "    feminine = sum(in_decade.gender == 'f')\n",
    "    pct = feminine / (masculine + feminine)\n",
    "    usuk.append(pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FF+jXrx8iIiIwc+ZMAICWlhZcXFxw8uRJVFRUCLaNiYkBx3Ho2rWrVLmOjo5Q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TvFW351ruaCXUd2aWPsRj3X6Dm7n92pxFPa/7/9+uJkrKZNq6Onn/6fzgUY92L8EvOLAr2vLmpm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\nBdJyZgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1184a25c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')\n",
    "plt.figure(figsize = (9, 6))\n",
    "plt.xlim(1800,2010)\n",
    "plt.ylim(0.01, 0.6)\n",
    "downward = np.array(percentages) - np.array(minima)\n",
    "upward = np.array(maxima) - np.array(percentages)\n",
    "plt.errorbar(timeaxis, percentages, yerr = [downward, upward], fmt='--o', \n",
    "             ecolor = 'k', color = 'b', label = 'titles by women in random sample of titles')\n",
    "plt.plot(weighttimes, weightpcts, 'g:', label = 'titles by women in weighted subset')\n",
    "plt.plot(timeaxis, usuk, 'r:', label = 'titles by women, us/uk only')\n",
    "plt.legend(loc='upper right')\n",
    "plt.savefig('figures/titlesubset/pctwomentwodatasets.png', bbox_inches = 'tight', dpi = 400)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IfWwA5JTuZZVFREREpHAq4iUfz2WXk7hiLTl1LwcgaO5bhHe/A1JTLU4mIiIi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F0bTWYTJu55A3uu7ZbEE7POYv6xeZIZZZkgQxPvYNzQ/C0Z271+JC6N/Bs1XGtI4nKZ9I1UZXX3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\noF2xp6zI04oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117cc2f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "timeline = []\n",
    "usa = []\n",
    "uk = []\n",
    "other = []\n",
    "minima = []\n",
    "maxima = []\n",
    "\n",
    "\n",
    "for decade in range(1800, 2010, 15):\n",
    "    in_decade = all.loc[(all.firstpub >= decade) & (all.firstpub < (decade + 15)) & ((all.isfic == 'fic')), : ]\n",
    "    numvols = len(in_decade)\n",
    "    usexamples = sum(in_decade.nationality == 'us') \n",
    "    ukexamples = sum(in_decade.nationality == 'uk')\n",
    "    unknown = sum(pd.isnull(in_decade.nationality))\n",
    "    otherex = numvols - (usexamples + ukexamples + unknown)\n",
    "    usa.append(usexamples / numvols)\n",
    "    uk.append(ukexamples / numvols)\n",
    "    other.append(otherex / numvols)\n",
    "    minimum, maximum = bootstrap_ratio(usexamples, (numvols - usexamples))\n",
    "    minima.append(minimum)\n",
    "    maxima.append(maximum)\n",
    "    timeline.append(np.mean(in_decade.firstpub))\n",
    "\n",
    "downward = np.array(usa) - np.array(minima)\n",
    "upward = np.array(maxima) - np.array(usa)\n",
    "plt.figure(figsize = (9, 6))\n",
    "plt.xlim(1800,2010)\n",
    "plt.ylim(-0.03, 0.72)\n",
    "plt.errorbar(timeline, usa, yerr = [downward, upward], fmt='--o', ecolor = 'k', color = 'b', label = \"USA\")\n",
    "plt.plot(timeline, uk, color = 'r', label = \"UK\")\n",
    "plt.plot(timeline, other, 'g:', label = 'other')\n",
    "plt.legend(loc='upper right')\n",
    "sns.set(font_scale= 1.3)\n",
    "plt.savefig('figures/titlesubset/pctusa.png', dpi = 400, bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
